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Machine learning research

Hafsteinn Einarsson

Associate Professor, University of Iceland
Principal Scientist, Amgen deCODE

I lead a research group working on natural language processing for Icelandic and Faroese, with active projects in computer vision for the natural sciences, clinical AI, and human genetics.

Hafsteinn Einarsson

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Selected publications

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NLP for Icelandic & Germanic

MazeEval: a benchmark for testing sequential decision-making in language models

Einarsson, H. · Proceedings of the fifteenth language resources and evaluation conference (LREC 2026) · 2026

MazeEval is a benchmark for testing sequential decision-making in language models through navigation tasks in procedurally generated mazes. The setup probes whether models can maintain a coherent internal map across multiple turns of interaction rather than relying on local pattern matching. Single-author paper published at LREC 2026.

Machine Learning · Natural Sciences

Temporal aggregation of vision-language features for high-accuracy fish classification in automated monitoring

Silva Martins, J. R., Bárðarson, H., Guðbrandsson, J., Einarsson, H. · Ecological Informatics · 2025

Aggregates temporal vision-language features from underwater video to classify fish species in automated monitoring streams, combining per-frame embeddings with temporal pooling to handle the noise and occlusion that limit frame-by-frame classifiers in deployed systems. Published in Ecological Informatics (2025) as part of the lab's ongoing computer-vision work with the Marine and Freshwater Research Institute.

NLP for Icelandic & Germanic

FoQA: A Faroese question-answering dataset

Simonsen, A., Nielsen, D. S., Einarsson, H. · Proceedings of the third workshop on resources and representations for under-resourced languages and domains (RESOURCEFUL-2025) · 2025

Introduces FoQA, the first dedicated question-answering dataset for Faroese, with extractive and generative variants. The dataset is constructed via translation and adaptation of existing QA benchmarks combined with native-speaker validation, and is accompanied by baseline evaluations of multilingual and Faroese- tuned language models. Establishes a reference benchmark for Faroese reading comprehension. RESOURCEFUL 2025 (NoDaLiDa co-located workshop).